Future aeronautical and aerospace systems will be highly dependent on Autonomy. Autonomy is often considered as automating the tasks to be performed by that system. Yet to achieve safe, dependable and survivable autonomous systems requires that the system is itself self-managing (selfconfiguring, self-healing, self-optimizing and self-protecting) as well as providing the functional autonomy. One such self-managing paradigm is autonomic computing. Biological inspired autonomous and autonomic systems (AAS) are essentially about creating self-directed and self-managing systems based on metaphors such as that of the autonomic nervous system. Agent technologies have been identified as a key enabler for engineering autonomy and autonomicity in systems, both in terms of retrofitting into legacy systems and designing new systems. Handing over responsibility to the systems raises concerns for humans.
Prior patents describe a technique for achieving security in agent based systems through the use of apoptosis, that is, the predetermined “death” of an agent unless it receives a reprieve or “stay alive” signal. This mimics the mechanism of cell death in the human (and animal) body, and hence makes use of autonomic and other biologically inspired metaphors. The technique may also be used to send “self destruct” signals to agents (or their current hosts) that may be compromised, or which cannot be identified as “friendly” or as having a right to access certain resources (re ALice signal disclosure).
A synthetic neural system is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. Biological systems inspire system design in many other ways as well, for example reflex reaction and health signs, nature inspired systems (NIS), hive and swarm behavior, and fire flies. These synthetic systems provide an autonomic computing entity that can be arranged to manage complexity, continuous self-adjust, adjustment to unpredictable conditions, and prevention and recovery for failures.
One key element is the general architecture of the etic neural system. A synthetic neural system is composed of a large number of highly interconnected processing autonomic elements that may be analogous to neurons in a brain working in parallel to solve specific problems. Unlike general purpose brains, a synthetic neural system is typically configured for a specific application and sometimes for a limited duration.
In one application of autonomic elements, each of a number of spacecrafts could be a worker in an autonomous space mission. The space mission can be configured as an autonomous nanotechnology swarm (ANTS). Each spacecraft in an ANTS has a specialized mission, much like ants in an ant colony have a specialized mission. Yet, a heuristic neural system (HNS) architecture of each worker in an ANTS provides coordination and interaction between each HNS that yields performance of the aggregate of the ANTS that exceeds the performance of a group of generalist workers.
More specifically, subset neural basis functions (SNBFs) within a HNS can have a hierarchical interaction among themselves much as the workers do in the entire ANTS collective. Hence, although many activities of the spacecraft could be controlled by individual SNBFs, a ruler SNBF could coordinate all of the SNBFs to assure that spacecraft objectives are met. Additionally, to have edundancy for the mission, inactive workers and rulers can only participate if a member of their type is lost.
In some situations, the ANTS encounters a challenging situation. For example, in some instances, the operation of a particular autonomic spacecraft can be detrimental either to the autonomic spacecraft or to the mission. It would be desirable to have a self-destruct mechanism that can be employed to avoid such a detrimental outcome, for example, analogous to apoptotic activity in a biological system. The need to replace the agent or spacecraft, and how to select an agent to become the replacement, form bases for various embodiments of the present teachings. Protecting scientific data obtained from such systems also forms the basis for various embodiments of the present teachings.